Posts
Dec, 28
A Generic Inverted Index Framework for Similarity Search on the GPU
We propose a novel generic inverted index framework on the GPU (called GENIE), aiming to reduce the programming complexity of the GPU for parallel similarity search of different data types. Not every data type and similarity measure are supported by GENIE, but many popular ones are. We present the system design of GENIE, and demonstrate […]
Dec, 24
Pass a Pointer: Exploring Shared Virtual Memory Abstractions in OpenCL Tools for FPGAs
Heterogeneous CPU-FPGA systems are gaining momentum in the embedded systems sector and in the data center market. While the programming abstractions for implementing the data transfer between CPU and FPGA (and vice versa) that are available in today’s commercial programming tools are well-suited for certain types of applications, the CPU-FPGA communication for applications that share […]
Dec, 24
Extending OmpSs for OpenCL kernel co-execution in heterogeneous systems
Heterogeneous systems have a very high potential performance but present difficulties in their programming. OmpSs is a well known framework for task based parallel applications, which is an interesting tool to simplify the programming of these systems. However, it does not support the co-execution of a single OpenCL kernel instance on several compute devices. To […]
Dec, 24
An MPI-Based Python Framework for Distributed Training with Keras
We present a lightweight Python framework for distributed training of neural networks on multiple GPUs or CPUs. The framework is built on the popular Keras machine learning library. The Message Passing Interface (MPI) protocol is used to coordinate the training process, and the system is well suited for job submission at supercomputing sites. We detail […]
Dec, 24
An In-depth Performance Characterization of CPU- and GPU-based DNN Training on Modern Architectures
Traditionally, Deep Learning (DL) frameworks like Caffe, TensorFlow, and Cognitive Toolkit exploited GPUs to accelerate the training process. This has been primarily achieved by aggressive improvements in parallel hardware as well as through sophisticated software frameworks like cuDNN and cuBLAS. However, recent enhancements to CPU-based hardware and software has the potential to significantly enhance the […]
Dec, 24
GAMER-2: a GPU-accelerated adaptive mesh refinement code — accuracy, performance, and scalability
We present GAMER-2, a GPU-accelerated adaptive mesh refinement (AMR) code for astrophysics. It provides a rich set of features, including adaptive time-stepping, several hydrodynamic schemes, magnetohydrodynamics, self-gravity, particles, star formation, chemistry and radiative processes with GRACKLE, data analysis with yt, and memory pool for efficient object allocation. GAMER-2 is fully bitwise reproducible. For the performance […]
Dec, 19
Molecular dynamics recipes for genome research
Molecular dynamics (MD) simulation allows one to predict the time evolution of a system of interacting particles. It is widely used in physics, chemistry and biology to address specific questions about the structural properties and dynamical mechanisms of model systems. MD earned a great success in genome research, as it proved to be beneficial in […]
Dec, 19
Accelerated Sparse Matrix Operations in Nonlinear Least Squares Solvers
This thesis focuses on data structures for sparse block matrices and the associated algorithms for performing linear algebra operations that I have developed. Sparse block matrices occur naturally in many key problems, such as Nonlinear LEast Squares (NLS) on graphical models. NLS are used by e.g. Simultaneous Localization and Mapping (SLAM) in robotics, Bundle Adjustment […]
Dec, 19
OpenCL-accelerated Point Feature Histogram and Its Application in Railway Track Point Cloud Data Processing
To meet the requirements of railway track point cloud processing, an OpenCL-accelerated Point Feature Histogram method is proposed using heterogeneous computing to improve the low computation efficiency. According to the characteristics of parallel computing of OpenCL, the data structure for point cloud storage is reconfigured. With the kernel performance analysis by CodeXL, the data reading […]
Dec, 19
Tactics to Directly Map CNN graphs on Embedded FPGAs
Deep Convolutional Neural Networks (CNNs) are the state-of-the-art in image classification. Since CNN feed forward propagation involves highly regular parallel computation, it benefits from a significant speed-up when running on fine grain parallel programmable logic devices. As a consequence, several studies have proposed FPGA-based accelerators for CNNs. However, because of the large computational power required […]
Dec, 19
Improving 3D Lattice Boltzmann Method stencil with asynchronous transfers on many-core processors
CPU-based many-core processors present an alternative to multicore CPU and GPU processors. In particular, the 93-Petaflops Sunway supercomputer, built from clustered many-core processors, has opened a new era for high performance computing that does not rely on GPU acceleration. However, memory bandwidth remains the main challenge for these architectures. This motivates our endeavor for optimizing […]
Dec, 15
POMPEI: Programming with OpenMP4 for Exascale Investigations
The objective of the Programming with OpenMP4 for Exascale Investigations (POMPEI) project is to explore new task-based programming techniques together with data structure centric programming for scientific applications to harness the potential of extreme-scale systems. Tasking is a well established by now approach on such systems as it has been used successfully to handle their […]
